getting(torch.cuda.FloatTensor)和weight type(torch.FloatTensor)应该是一样的
Getting (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, down=True, use_act=True, **kwargs):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, padding_mode="reflect", **kwargs)
if down
else nn.ConvTranspose2d(in_channels, out_channels, **kwargs),
nn.InstanceNorm2d(out_channels),
nn.ReLU(inplace=True) if use_act else nn.Identity()
)
def forward(self, x):
return self.conv(x)
class ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.block = nn.Sequential(
ConvBlock(channels, channels, kernel_size=3, padding=1),
ConvBlock(channels, channels, use_act=False, kernel_size=3, padding=1),
)
def forward(self, x):
return x + self.block(x)
class Generator(nn.Module):
def __init__(self, image_channels, num_features= 64, num_residuals=9):
super().__init__()
self.initial = nn.Sequential(
nn.Conv2d(image_channels, num_features, kernel_size=7, stride=1, padding=3, padding_mode="reflect"),
nn.ReLU(inplace=True)
)
self.down_blocks = nn.ModuleList = ([
ConvBlock(num_features, num_features*2, kernel_size=3, stride=2, padding=1),
ConvBlock(num_features*2, num_features*4, kernel_size=3, stride=2, padding=1),
])
self.residual_blocks = nn.Sequential(
*[ResidualBlock(num_features*4) for _ in range(num_residuals)]
)
self.up_blocks = nn.ModuleList = ([
ConvBlock(num_features*4, num_features*2, down=False, kernel_size=3, padding=1, stride=2, output_padding=1),
ConvBlock(num_features*2, num_features, down=False, kernel_size=3, padding=1, stride=2, output_padding=1),
])
self.last = nn.Conv2d(num_features, image_channels, kernel_size=7, stride=1, padding=3, padding_mode="reflect")
def forward(self, x):
x = self.initial(x)
for layer in self.down_blocks:
x = layer(x)
x = self.residual_blocks(x)
for layer in self.up_blocks:
x = layer(x)
return torch.tanh(self.last(x))
img_channels = 3
img_size = 256
x = torch.randn((2, img_channels, img_size, img_size))
x = x.to(DEVICE)
gen = Generator(img_channels, 9).to(DEVICE)
print(gen(x).shape)
我已经为 Cycle GAN 实现了这个模型。此处使用的输入仅用于演示目的以缩短代码，但实际输入会引发相同的错误。代码在 CPU 上运行良好，但当我将其转移到 GPU 时，出现以下错误：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-30-a56669856674> in <module>
4 x = x.to(DEVICE)
5 gen = Generator(img_channels, 9).to(DEVICE)
----> 6 print(gen(x).shape)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
<ipython-input-25-f9a41f6c9d12> in forward(self, x)
26
27 for layer in self.down_blocks:
---> 28 x = layer(x)
29
30 x = self.residual_blocks(x)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
<ipython-input-23-e139087b9df4> in forward(self, x)
11
12 def forward(self, x):
---> 13 return self.conv(x)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
421
422 def forward(self, input: Tensor) -> Tensor:
--> 423 return self._conv_forward(input, self.weight)
424
425 class Conv3d(_ConvNd):
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
416 return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
417 weight, self.bias, self.stride,
--> 418 _pair(0), self.dilation, self.groups)
419 return F.conv2d(input, weight, self.bias, self.stride,
420 self.padding, self.dilation, self.groups)
RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
我的模型正在更改为设备 (CUDA) 和输入。我不知道现在是什么问题
您的代码中有拼写错误：
self.down_blocks = nn.ModuleList = ([
...
self.up_blocks = nn.ModuleList = ([
应该是：
self.down_blocks = nn.ModuleList([
...
self.up_blocks = nn.ModuleList([
您需要重新加载内核，因为此时您实际上已经将 nn.ModuleList 覆盖为 list.
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, down=True, use_act=True, **kwargs):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, padding_mode="reflect", **kwargs)
if down
else nn.ConvTranspose2d(in_channels, out_channels, **kwargs),
nn.InstanceNorm2d(out_channels),
nn.ReLU(inplace=True) if use_act else nn.Identity()
)
def forward(self, x):
return self.conv(x)
class ResidualBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.block = nn.Sequential(
ConvBlock(channels, channels, kernel_size=3, padding=1),
ConvBlock(channels, channels, use_act=False, kernel_size=3, padding=1),
)
def forward(self, x):
return x + self.block(x)
class Generator(nn.Module):
def __init__(self, image_channels, num_features= 64, num_residuals=9):
super().__init__()
self.initial = nn.Sequential(
nn.Conv2d(image_channels, num_features, kernel_size=7, stride=1, padding=3, padding_mode="reflect"),
nn.ReLU(inplace=True)
)
self.down_blocks = nn.ModuleList = ([
ConvBlock(num_features, num_features*2, kernel_size=3, stride=2, padding=1),
ConvBlock(num_features*2, num_features*4, kernel_size=3, stride=2, padding=1),
])
self.residual_blocks = nn.Sequential(
*[ResidualBlock(num_features*4) for _ in range(num_residuals)]
)
self.up_blocks = nn.ModuleList = ([
ConvBlock(num_features*4, num_features*2, down=False, kernel_size=3, padding=1, stride=2, output_padding=1),
ConvBlock(num_features*2, num_features, down=False, kernel_size=3, padding=1, stride=2, output_padding=1),
])
self.last = nn.Conv2d(num_features, image_channels, kernel_size=7, stride=1, padding=3, padding_mode="reflect")
def forward(self, x):
x = self.initial(x)
for layer in self.down_blocks:
x = layer(x)
x = self.residual_blocks(x)
for layer in self.up_blocks:
x = layer(x)
return torch.tanh(self.last(x))
img_channels = 3
img_size = 256
x = torch.randn((2, img_channels, img_size, img_size))
x = x.to(DEVICE)
gen = Generator(img_channels, 9).to(DEVICE)
print(gen(x).shape)
我已经为 Cycle GAN 实现了这个模型。此处使用的输入仅用于演示目的以缩短代码，但实际输入会引发相同的错误。代码在 CPU 上运行良好，但当我将其转移到 GPU 时，出现以下错误：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-30-a56669856674> in <module>
4 x = x.to(DEVICE)
5 gen = Generator(img_channels, 9).to(DEVICE)
----> 6 print(gen(x).shape)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
<ipython-input-25-f9a41f6c9d12> in forward(self, x)
26
27 for layer in self.down_blocks:
---> 28 x = layer(x)
29
30 x = self.residual_blocks(x)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
<ipython-input-23-e139087b9df4> in forward(self, x)
11
12 def forward(self, x):
---> 13 return self.conv(x)
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py in forward(self, input)
421
422 def forward(self, input: Tensor) -> Tensor:
--> 423 return self._conv_forward(input, self.weight)
424
425 class Conv3d(_ConvNd):
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
416 return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
417 weight, self.bias, self.stride,
--> 418 _pair(0), self.dilation, self.groups)
419 return F.conv2d(input, weight, self.bias, self.stride,
420 self.padding, self.dilation, self.groups)
RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
我的模型正在更改为设备 (CUDA) 和输入。我不知道现在是什么问题
您的代码中有拼写错误：
self.down_blocks = nn.ModuleList = ([
...
self.up_blocks = nn.ModuleList = ([
应该是：
self.down_blocks = nn.ModuleList([
...
self.up_blocks = nn.ModuleList([
您需要重新加载内核，因为此时您实际上已经将 nn.ModuleList 覆盖为 list.